Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems

By Gaurav Dhiman, Vijay Kumar. Knowledge-Based Systems, 2019

1,092 citations recorded by the source corpus.

Abstract This paper presents a novel bio-inspired algorithm called Seagull Optimization Algorithm (SOA) for solving computationally expensive problems. The main inspiration of this algorithm is the migration and attacking behaviors of a seagull in nature. These behaviors are mathematically modeled and implemented to emphasize exploration and exploitation in a given search space. The performance of SOA algorithm is compared with nine well-known metaheuristics on forty-four benchmark test functions. The analysis of computational complexity and convergence behaviors of the proposed algorithm have been evaluated. It is then employed to solve seven constrained real-life industrial applications to demonstrate its applicability. Experimental results reveal that the proposed algorithm is able to solve challenging large-scale constrained problems and is very competitive algorithm as compared with other optimization algorithms.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/0f08ab72784a9fa39a7e7186e827c4f0beff8e5e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Amandeep Kaur
2. Gaurav Dhiman
3. Vijay Kumar

Citations in the corpus

• STOA: A bio-inspired based optimization algorithm for industrial engineering problems (2019, background): “…problems (Chandrawat et al., 2017; Singh and Dhiman, 2017; Kaur and Dhiman, 2019; Singh and Dhiman, 2018b; Singh et al., 2018b,a; Kaur et al., 2018; Dhiman et al., 2018, 2019; Dhiman and Kumar, 2018) have high computational cost, nonlinear constraints, non-convex, and huge amount of solution…”